In [1]:
import pandas as pd
import plotly.express as px

# 1) Load data and quick preview
marketing_summary = pd.read_csv("/Users/gillianmondero/Downloads/marketing_summary.csv")
marketing_summary["date"] = pd.to_datetime(marketing_summary["date"])
marketing_summary["conversion_rate"] = (
    marketing_summary["new_customers"] / marketing_summary["users_active"]
).fillna(0)

print("Data preview:")
print(marketing_summary.head(), "\n")
Data preview:
        date  users_active  total_sales  new_customers  report_generated  \
0 2023-06-01           179     81287.31              9  2023-06-01 16:00   
1 2023-06-02            67     74771.99              5  2023-06-02 16:00   
2 2023-06-03           369     84809.74             11  2023-06-03 16:00   
3 2023-06-04            94     61212.30              3  2023-06-04 16:00   
4 2023-06-05           402     80911.49             10  2023-06-05 16:00   

   col_6   col_7   col_8  col_9  col_10  ...  col_42  col_43  col_44  col_45  \
0  North  519.37     NaN  North  987.06  ...    West     NaN   756.0   South   
1   East     NaN    75.0  South  916.36  ...    West  457.14     NaN    West   
2    NaN  512.79  2643.0   East  420.43  ...   South  161.89  3339.0   South   
3  South     NaN  1264.0  North   16.13  ...   North  661.14     NaN     NaN   
4  North  965.78  1108.0  South  147.12  ...    West  222.50   394.0   South   

   col_46  col_47  col_48  col_49  col_50  conversion_rate  
0   81.97     NaN    East     NaN     NaN         0.050279  
1  482.41  1327.0    East     NaN  4506.0         0.074627  
2   18.71  3484.0    East  753.98   463.0         0.029810  
3     NaN  4517.0   North     NaN  2294.0         0.031915  
4  498.87  3364.0     NaN  678.05     NaN         0.024876  

[5 rows x 51 columns] 

In [2]:
# 2) Basic KPI totals
tot_sales   = marketing_summary["total_sales"].sum()
tot_active  = marketing_summary["users_active"].sum()
tot_newcust = marketing_summary["new_customers"].sum()
avg_conv    = marketing_summary["conversion_rate"].mean()

print("KPI numbers:")
print(f"Total Sales    : ₱{tot_sales:,.2f}")
print(f"Active Users   : {tot_active:,}")
print(f"New Customers  : {tot_newcust:,}")
print(f"Avg Conversion : {avg_conv:.2%}\n")
KPI numbers:
Total Sales    : ₱5,767,580.45
Active Users   : 27,331
New Customers  : 765
Avg Conversion : 3.75%

In [5]:
# 3) Visualisations + save to JPG
fig1 = px.line(marketing_summary, x="date", y="total_sales",
               title="Total Sales Over Time")
fig1.show()
fig1.write_image("total_sales_over_time.jpg", format="jpg", scale=2)

fig2 = px.line(marketing_summary, x="date", y="users_active",
               title="Daily Active Users")
fig2.show()
fig2.write_image("daily_active_users.jpg", format="jpg", scale=2)

fig3 = px.bar(marketing_summary, x="date", y="new_customers",
              title="New Customers Per Day")
fig3.show()
fig3.write_image("new_customers_per_day.jpg", format="jpg", scale=2)

fig4 = px.line(marketing_summary, x="date", y="conversion_rate",
               title="Conversion Rate Over Time")
fig4.show()
fig4.write_image("conversion_rate_over_time.jpg", format="jpg", scale=2)
In [ ]: